关键词: DOMAIN, NeuralEnsembles, 模型耦合, 潜在分布模拟, 气候变化, 毛竹

Abstract:

In this paper a profile technique- DOMAIN was used to map potential habitat suitable for moso bamboo (Phyllostachys edulis). and to select the areas with low suitable habitat as pseudo-absences. Then a group discrimination technique-NeuralEnsembles was employed to predict the potential distribution of moso bamboo (hereafter termed hybrid model) based on pseudo-absences and true presences data. Sensitivity, Kappa and the area under the curve (AUC) values of receiver operator characteristic (ROC) curve were employed to assess model predictive accuracy. Meanwhile, we investigated the sample size effects of pseudo-absences generated by DOMAIN on model performance. We also compared model performance of hybrid model with single model-NeurnalEnsembles. Results indicated that the hybrid model could achieve a higher accuracy in simulating current distribution of moso bamboo in comparison to single model. Sensitivity and AUC were relatively independent from pseudo-absence sample size, but Kappa declined with the increasing pseudo-absence sample size. Climate change is likely to have dramatic effects on the potential distribution of moso bamboo, with the northward migration ranging from 33 to 266 km, and the area expansion by 7.4% to 13.9%.

Key words: DOMAIN, NeuralEnsembles, hybrid model, potential distribution modeling, climate change, Phyllostachys edulis